Machine intelligence is useful to gain insights to a large quantity of data that is undecipherable to human comprehension. Machine intelligence, also known as artificial intelligence, can encompass machine learning analysis (e.g., supervised machine learning, unsupervised machine learning, or deep machine learning), natural language parsing and processing, computer perception, or any combination thereof. Machine intelligence (or learning) can facilitate studies and researches yielding specialized insights that are normally not attainable by human mental exercises.
Machine intelligence can be used to analyze digital conversations, publications, or other content that human beings may generate (“user generated content”). The digital conversations, publications, or other user-generated content can be collectively referred to as “digital chatter.” For example, a machine intelligence analysis engine can identify patterns in the digital chatter that may be pertinent in making real-world decisions or otherwise recognizing patterns. This process is often referred to as “data mining.” Various application services of the social networking system capture, derive or generate information that may be relevant to a machine intelligence analysis engine. Various application services of the social networking system can benefit from the insights produced by the machine intelligence analysis engine. However, because of the distributed nature of the application services in a social networking system, it is computationally expensive and challenging to timely produce insights (e.g., identify patterns) before the insights or the patterns become irrelevant.
The figures show various embodiments of this disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of embodiments described herein.